1. March 11 - 15, 2007ACM SAC, Seoul, Korea1 IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems Aliaksandr Birukou, Enrico Blanzieri, Vincenzo D'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena

2. March 11 - 15, 2007ACM SAC, Seoul, Korea2 Introduction Recommendation systems Service-Oriented Computing Implicit Culture System for Implicit Culture Support (SICS) SICS Architecture Main modules Configuration Applications Web service discovery Conclusions References

3. March 11 - 15, 2007ACM SAC, Seoul, Korea3 Recommendation systems Prune large information spaces in searching for items of interest Examples movies (MovieLens), music (JUKE-BOX), books (Amazon), hotels (TripAdvisor) … Meta-recommendation systems Work with data from multiple (heterogeneous) information sources MetaLens [Schafer et al., 2002]

4. March 11 - 15, 2007ACM SAC, Seoul, Korea4 Service-oriented computing Service Registry Service Client Service Provider Publish Bind Find Service-oriented application Web service description Web service Requirements for a recommendation service: Use in various application domains Ability to store heterogeneous client data Adaptability to the needs of a particular client Ability to process data according to the domain specific rules

5. March 11 - 15, 2007ACM SAC, Seoul, Korea5 Implicit Culture (IC): motivation and goals Communities of human/artificial agents have knowledge specific to their activities, i.e., community culture The knowledge is often implicit and highly personalized Encourage a newcomer to behave according to a community culture Transfer knowledge implicitly (without special efforts for its analysis and description) http://www.dit.unitn.it/~implicit [Blanzieri et al., 2001]

6. March 11 - 15, 2007ACM SAC, Seoul, Korea6 IC definitions Action – something that can be done Agent (actor) – somebody or something performing an action Object – something that passively participate in the action Situation – a state of the world faced by the agent. Includes a set of objects and a set of possible actions Culture – a usual behavior of the group of agents Group G – group of agents which behaviour is observed Group G' – group of agents who require recommendations Implicit Culture relation – situations in which agents of the group G behave similarly to agents of the group G' System for Implicit Culture Support (SICS) – a system which tries to establish IC relation Observe agents’ actions Extract actions performed in different situations Suggest actions in a given situation

7. March 11 - 15, 2007ACM SAC, Seoul, Korea7 System for Implicit Culture Support (SICS) Stores information about actions Produce a theory about common user behavior Produce recommendation about action

8. March 11 - 15, 2007ACM SAC, Seoul, Korea8 SICS Architecture SICS Core SICS layer infers theory rules and recommends actions Configuration and storage layer manages theory SICS Remote Module defines protocols for information exchange with the client SICS Remote Client provides a simple interface for remote clients

9. March 11 - 15, 2007ACM SAC, Seoul, Korea9 Storage Module Theory rules if consequent (predicates) then antecedent (predicates) Predicates: Conditions on observations (action- predicates) Conditions on time (temporal-predicates) Observations Agents (1…N), Actions (1), Objects (0…N), Attributes (0…N) Scenes (1…N) no agents no timestamps

10. March 11 - 15, 2007ACM SAC, Seoul, Korea10 Inductive Module Analyses observations and generates theory rules for an actor or a group of actors “Apriori” algorithm for mining association rules [Agrawal & Srikant, 1994] A transaction is a sequence of executed actions A 1,…,A N (can be obtained from observations using timestamps) An association rule is an implication of the form A 1  A 2 where A 1, A 2 are actions, A 1  A 2 The rule holds with confidence c if c% of transactions that contain A 1 also contain A 2 The rule A 1  A 2 has support s in the transaction set s% of transactions contain A 1  A 2 Generate association rules that have support and confidence greater than predefined minimum support and minimum confidence.

11. March 11 - 15, 2007ACM SAC, Seoul, Korea11 Composer Module Cultural Action Finder (CAF) Matches actions executed by agents from group G’ with antecedents of the theory rules Matching algorithms Returns consequences of the theory rules (cultural actions) Scene producer Finds a set of agents that have performed actions similar to a cultural action for the agent X Selects a set of agents similar to an agent X and a set of scenes S in which they have performed the actions Select and propose to X a scene from S

12. March 11 - 15, 2007ACM SAC, Seoul, Korea12 Instance Configuration Configuration of similarity functions: Rules for calculating similarity among observations Similarity weights for elements (names and values) exceptions, instants and default Case sensitive or not Regular expressions Inductive Module constants Composer constants: Similarity threshold Number of nearest neighbors Return all scenes or only the best Max number of observations Names of groups G and G’

13. March 11 - 15, 2007ACM SAC, Seoul, Korea13 Applications Prototypes: Recommending Web links [Birukou et al., 2005] Recommending scientific publications Quality-based Indexing of Web Information (QUIEW) http://quiew.itc.it/http://quiew.itc.it/ Supporting Polymerase Chain Reaction (PCR) experiments [Mullis et al., 1986] [Sarini et al., 2004] Software patterns selection Web service discovery

14. March 11 - 15, 2007ACM SAC, Seoul, Korea14 Web Service (WS) discovery Meeting functionality required by a user with specifications of existing web services Problems: incomplete specifications, broken links, unfair providers… Choosing a service with good quality characteristics Problems: often QoS data are not available, some of them are context-dependent… Implicit Culture approach Analyze which web services have been previously used for similar problems by clients with similar interests Use up-to-date information to improve service discovery and QoS-driven selection

15. March 11 - 15, 2007ACM SAC, Seoul, Korea15 A system for WS discovery Search process Monitorin g process

16. March 11 - 15, 2007ACM SAC, Seoul, Korea16 WS discovery in terms of IC Observations Actors Applications (application name, user name, location) Users (user name, location) Objects Operations (operation name, web service name) Inputs/Outputs (parameter name, parameter value) Requests (goals, operations, inputs/outputs) Actions Invoke (timestamp, operation, input) Get response (timestamp, operation, output, response time) Raise exception (timestamp, operation, exception type, input) Provide feedback (timestamp, QoS parameters) Submit request (timestamp, request) Rules if submit request (request) then invoke (operation-X(service-Y), request). Similarity measures: Vector Space Model (VSM) Term Frequency- Inverse Document Frequency (TF-IDF) metric WordNet-based semantic similarity measure

17. March 11 - 15, 2007ACM SAC, Seoul, Korea17 A system for WS discovery: experimental results 20 web services (http://www.xMethods.com) divided into 5 categories [Kokash et al., 2007]http://www.xMethods.com 4 clients submit 100 requests VSM WordNet

18. March 11 - 15, 2007ACM SAC, Seoul, Korea18 Conclusions Ubiquity The IC-service can be accessed from any workplace Reusability A unique solution for various distributed communities Integration The knowledge transfer between communities is facilitated Scalability 100000 observations of 100 users for one instance Composition of several IC-Services is possible Portability XML storage Customization Ability of runtime configuring of theory rules…

19. March 11 - 15, 2007ACM SAC, Seoul, Korea19 References [Schafer et al., 2002] J. B. Schafer, J. A. Konstan, and J. Riedl. Meta- recommendation systems: user-controlled integration of diverse recommendations. In Proc. of the Int. Conference on Information and Knowledge Management, pages 43- 51. ACM Press, 2002. [Blanzieri et al., 2001] E. Blanzieri, P. Giorgini, P. Massa, and S. Recla. Implicit culture for multi-agent interaction support. In CooplS: Proc. of the 9th Int. Conference on Cooperative Information Systems, volume 2172 of LNCS, pages 27-39. Springer, 2001. [Birukov et al., 2005] A. Birukov, E. Blanzieri, and P. Giorgini. Implicit: An agent- based recommendation system for web search. In AAMAS: Proc. of the 4th Int. Joint Conference on Autonomous Agents and Multiagent Systems, pages 618-624. ACM Press, 2005. [Mullis et al., 1986] K. B. Mullis, F. A. Faloona, S. Scharf, R. K. Saiki, G. Horn, H. A. Erlich. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. In Cold Spring Harbor Symposia on Quantitative Biology, volume 51, pages 263-273, 1986. [Sarini et al., 2004] M. Sarini, E. Blanzieri, P. Giorgini, C. Moser. From actions to suggestions: supporting the work of biologists through laboratory notebooks. In COOP: Proc. of 6th Int. Conference on the Design of Cooperative Systems, pages 131-146. IOS Press, 2004. [Agrawal & Srikant, 1994] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB: Proc. of the 20th Int. Conference on Very Large Data Bases, pages 487-499. Morgan Kaufmann, 1994. [Kokash et al., 2007] N. Kokash, A. Birukou, V. D'Andrea: Web service discovery based on past user experience. In: International Conference on Business Information Systems (BIS), to appear, Springer (2007)